Import and process data

Memory

RL modeling

Choice weights from model with partial counterfactual learning

Figure: Distribution of choice weights from CF model

Model: Choice weights by block condition and age

	est
Predictors	Estimates	CI
abstraction1	-0.4781	-0.5546 – -0.4017
reward condition1	0.2408	0.1643 – 0.3172
age scaled	0.1915	0.0912 – 0.2918
abstraction1 × reward condition1	0.3327	0.2562 – 0.4091
abstraction1 × age scaled	-0.0707	-0.1472 – 0.0059
reward condition1 × age scaled	0.0568	-0.0197 – 0.1334
(abstraction1 × reward condition1) × age scaled	0.0353	-0.0413 – 0.1118
Random Effects
σ²	0.92
τ₀₀ _{subject_id}	0.16
ICC	0.15
N _{subject_id}	151
Observations	604
Marginal R² / Conditional R²	0.291 / 0.399

Model: Exemplar choice weights across conditions

	est
Predictors	Estimates	CI
reward condition1	-0.0919	-0.1787 – -0.0050
Random Effects
σ²	0.59
τ₀₀ _{subject_id}	0.61
ICC	0.51
N _{subject_id}	151
Observations	302
Marginal R² / Conditional R²	0.007 / 0.513

Model: Category choice weights across conditions

	est
Predictors	Estimates	CI
reward condition1	0.5734	0.4692 – 0.6776
Random Effects
σ²	0.85
τ₀₀ _{subject_id}	0.19
ICC	0.19
N _{subject_id}	151
Observations	302
Marginal R² / Conditional R²	0.241 / 0.382

Relations between choice weights and points earned

## 
## Call:
## lm(formula = total_points ~ est, data = beta_ests_points_c_c)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -267.278  -36.123    4.793   38.580   96.301 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  104.105      7.636  13.634  < 2e-16 ***
## est           36.489      4.757   7.671 2.06e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57.31 on 149 degrees of freedom
## Multiple R-squared:  0.2831, Adjusted R-squared:  0.2783 
## F-statistic: 58.84 on 1 and 149 DF,  p-value: 2.056e-12

## 
## Call:
## lm(formula = total_points ~ est, data = beta_ests_points_e_c)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -272.592  -35.015    8.594   44.639  132.400 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  136.796      8.487  16.117   <2e-16 ***
## est            8.763      4.177   2.098   0.0376 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 66.71 on 149 degrees of freedom
## Multiple R-squared:  0.02869,    Adjusted R-squared:  0.02218 
## F-statistic: 4.402 on 1 and 149 DF,  p-value: 0.03759

## 
## Call:
## lm(formula = total_points ~ est, data = beta_ests_points_c_e)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -324.49  -45.41   -0.46   56.62  190.35 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  158.110      7.078  22.338   <2e-16 ***
## est           -7.549      6.693  -1.128    0.261    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 86.38 on 149 degrees of freedom
## Multiple R-squared:  0.008467,   Adjusted R-squared:  0.001812 
## F-statistic: 1.272 on 1 and 149 DF,  p-value: 0.2611

## 
## Call:
## lm(formula = total_points ~ est, data = beta_ests_points_e_e)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -326.12  -31.20   -5.67   44.45  222.81 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   53.035     13.394   3.960 0.000116 ***
## est           59.651      6.925   8.614 9.38e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 70.87 on 149 degrees of freedom
## Multiple R-squared:  0.3324, Adjusted R-squared:  0.328 
## F-statistic:  74.2 on 1 and 149 DF,  p-value: 9.382e-15

Figure 2D: Effect of choice weights on points earned

Relations between learning and memory

Do choice weights relate to memory?

Model: AUC by age, exemplar choice weights, specificity, block condition

	AUC
Predictors	Estimates	CI
age scaled	0.0100	-0.0033 – 0.0232
beta scaled	0.0189	0.0093 – 0.0285
abstraction1	0.0611	0.0561 – 0.0661
reward condition1	-0.0055	-0.0106 – -0.0004
age scaled × beta scaled	0.0138	0.0026 – 0.0251
age scaled × abstraction1	0.0043	-0.0008 – 0.0093
beta scaled × abstraction1	-0.0064	-0.0119 – -0.0008
age scaled × reward condition1	-0.0009	-0.0060 – 0.0042
beta scaled × reward condition1	-0.0103	-0.0167 – -0.0040
abstraction1 × reward condition1	-0.0002	-0.0052 – 0.0048
age scaled × beta scaled × abstraction1	-0.0048	-0.0110 – 0.0014
age scaled × beta scaled × reward condition1	-0.0067	-0.0137 – 0.0002
age scaled × abstraction1 × reward condition1	0.0010	-0.0040 – 0.0060
beta scaled × abstraction1 × reward condition1	0.0004	-0.0052 – 0.0059
age scaled × beta scaled × abstraction1 × reward condition1	0.0042	-0.0019 – 0.0104
Random Effects
σ²	0.00
τ₀₀ _{subject_id}	0.01
ICC	0.61
N _{subject_id}	151
Observations	608
Marginal R² / Conditional R²	0.320 / 0.736

Figure 3C: AUC by exemplar choice weights - model effects

Model: AUC by age, category choice weights, specificity, block condition

	AUC
Predictors	Estimates	CI
age scaled	0.0190	0.0049 – 0.0331
beta scaled	-0.0018	-0.0101 – 0.0065
abstraction1	0.0588	0.0532 – 0.0645
reward condition1	-0.0067	-0.0132 – -0.0002
age scaled × beta scaled	-0.0028	-0.0112 – 0.0056
age scaled × abstraction1	0.0033	-0.0024 – 0.0090
beta scaled × abstraction1	0.0048	-0.0008 – 0.0105
age scaled × reward condition1	-0.0026	-0.0093 – 0.0041
beta scaled × reward condition1	0.0013	-0.0057 – 0.0083
abstraction1 × reward condition1	-0.0008	-0.0064 – 0.0048
age scaled × beta scaled × abstraction1	0.0009	-0.0048 – 0.0066
age scaled × beta scaled × reward condition1	-0.0058	-0.0128 – 0.0013
age scaled × abstraction1 × reward condition1	0.0005	-0.0052 – 0.0063
beta scaled × abstraction1 × reward condition1	0.0028	-0.0028 – 0.0085
age scaled × beta scaled × abstraction1 × reward condition1	-0.0038	-0.0095 – 0.0019
Random Effects
σ²	0.00
τ₀₀ _{subject_id}	0.01
ICC	0.63
N _{subject_id}	151
Observations	608
Marginal R² / Conditional R²	0.290 / 0.739

Memory Specificity Analyses: Experiment 1 (Partial CF Learning Model)

Kate Nussenbaum

6/13/24

Import and process data

Memory

RL modeling

Choice weights from model with partial counterfactual learning

Figure: Distribution of choice weights from CF model

Model: Choice weights by block condition and age

Model: Exemplar choice weights across conditions

Model: Category choice weights across conditions

Relations between choice weights and points earned

Figure 2D: Effect of choice weights on points earned

Relations between learning and memory

Do choice weights relate to memory?

Model: AUC by age, exemplar choice weights, specificity, block condition

Figure 3C: AUC by exemplar choice weights - model effects

Model: AUC by age, category choice weights, specificity, block condition

Figure 3C: AUC by category choice weights: model effects

Figure (for presentation): Category and exemplar memory by exemplar choice weights